GapMind for catabolism of small carbon sources

 

Aligments for a candidate for hutF in Sphingomonas koreensis DSMZ 15582

Align formimidoylglutamate deiminase (EC 3.5.3.13) (characterized)
to candidate Ga0059261_3965 Ga0059261_3965 formimidoylglutamate deiminase (EC 3.5.3.13)

Query= BRENDA::Q9HU77
         (453 letters)



>FitnessBrowser__Korea:Ga0059261_3965
          Length = 447

 Score =  434 bits (1116), Expect = e-126
 Identities = 229/449 (51%), Positives = 293/449 (65%), Gaps = 5/449 (1%)

Query: 4   IFAERALLPEGWARNVRFEISADGVLAEIRPDANADGAERLGGAVLPGMPNLHSHAFQRA 63
           ++ E ALL  GW+  VR  + ADG +  +    + +  +      LPG+PN+HSHAFQRA
Sbjct: 3   LWFESALLENGWSERVRLTL-ADGQIETVEAGVDPEAGDERHFVALPGIPNVHSHAFQRA 61

Query: 64  MAGLAEVAGNPNDSFWTWRELMYRMVARLSPEQIEVIACQLYIEMLKAGYTAVAEFHYVH 123
           MAGLAE  G  +D FW+WRELMYR V R+ PE+ E IA   Y EML++G+T V EFHY+H
Sbjct: 62  MAGLAEARGRADDDFWSWRELMYRFVGRIGPEECEAIAALAYAEMLESGFTRVGEFHYLH 121

Query: 124 HDLDGRSYADPAELSLRISRAASAAGIGLTLLPVLYSHAGFGGQPASEGQRRFINGSEAY 183
           H   G  Y D AE+S RI+ AA+A GI LTLLPV Y+H GFGGQPA   Q RF+N  + +
Sbjct: 122 HTPGGGRYDDVAEMSGRIAAAAAATGIALTLLPVFYAHGGFGGQPAGTAQARFLNDVDGF 181

Query: 184 LELLQRLRAPLEAAGHSLGLCFHSLRAVTPQQIATVLAAGHDDLPVHIHIAEQQKEVDDC 243
             L++R  A L + G  +G+  HSLRA TP ++  +LA   D  PVHIHIAEQ KEV DC
Sbjct: 182 AALVERAGATLASDG-VIGIAPHSLRAATPGELRALLAMA-DRGPVHIHIAEQVKEVADC 239

Query: 244 QAWSGRRPLQWLYENVAVDQRWCLVHATHADPAEVAAMARSGAVAGLCLSTEANLGDGIF 303
            AWSG+RP++WL +N+ VD RW LVHATH +P EVA +A SGAVAGLC  TEANLGDG+F
Sbjct: 240 VAWSGKRPVRWLLDNMPVDARWTLVHATHVEPGEVAGIAASGAVAGLCPITEANLGDGVF 299

Query: 304 PATDFLAQGGRLGIGSDSHVSLSVVEELRWLEYGQRLRDRKRNRLYRDDQPMIGRTLYDA 363
           PA +F+A GG +GIGSDS+V +   EELR LEYGQRL  R RN L   D+P  G  ++ A
Sbjct: 300 PAAEFMALGGMIGIGSDSNVRIDAAEELRLLEYGQRLTRRARNVLAGGDRPATGARMFAA 359

Query: 364 ALAGGAQALGQPIGSLAVGRRADLLVLDGNDPYLASAEGDALLNRWLFAGGDRQVRDVMV 423
           A+AGG ++LG   G LA GR AD++ L+ +DP  A  +GDAL++ W+FA     V  V  
Sbjct: 360 AVAGGGRSLGVETG-LAAGRPADIVSLNRDDPAFAERKGDALVDSWVFA-SRAGVDCVWR 417

Query: 424 AGRWVVRDGRHAGEERSARAFVQVLGELL 452
            G   V  GRH   +     +   L  L+
Sbjct: 418 GGAKQVAGGRHRDRDAIEARYRVALNRLM 446


Lambda     K      H
   0.321    0.136    0.415 

Gapped
Lambda     K      H
   0.267   0.0410    0.140 


Matrix: BLOSUM62
Gap Penalties: Existence: 11, Extension: 1
Number of Sequences: 1
Number of Hits to DB: 637
Number of extensions: 30
Number of successful extensions: 5
Number of sequences better than 1.0e-02: 1
Number of HSP's gapped: 1
Number of HSP's successfully gapped: 1
Length of query: 453
Length of database: 447
Length adjustment: 33
Effective length of query: 420
Effective length of database: 414
Effective search space:   173880
Effective search space used:   173880
Neighboring words threshold: 11
Window for multiple hits: 40
X1: 16 ( 7.4 bits)
X2: 38 (14.6 bits)
X3: 64 (24.7 bits)
S1: 41 (21.9 bits)
S2: 51 (24.3 bits)

Align candidate Ga0059261_3965 Ga0059261_3965 (formimidoylglutamate deiminase (EC 3.5.3.13))
to HMM TIGR02022 (hutF: formiminoglutamate deiminase (EC 3.5.3.13))

# hmmsearch :: search profile(s) against a sequence database
# HMMER 3.3.1 (Jul 2020); http://hmmer.org/
# Copyright (C) 2020 Howard Hughes Medical Institute.
# Freely distributed under the BSD open source license.
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# query HMM file:                  ../tmp/path.carbon/TIGR02022.hmm
# target sequence database:        /tmp/gapView.18805.genome.faa
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Query:       TIGR02022  [M=455]
Accession:   TIGR02022
Description: hutF: formiminoglutamate deiminase
Scores for complete sequences (score includes all domains):
   --- full sequence ---   --- best 1 domain ---    -#dom-
    E-value  score  bias    E-value  score  bias    exp  N  Sequence                                 Description
    ------- ------ -----    ------- ------ -----   ---- --  --------                                 -----------
   5.1e-189  614.6   4.1   5.7e-189  614.5   4.1    1.0  1  lcl|FitnessBrowser__Korea:Ga0059261_3965  Ga0059261_3965 formimidoylglutam


Domain annotation for each sequence (and alignments):
>> lcl|FitnessBrowser__Korea:Ga0059261_3965  Ga0059261_3965 formimidoylglutamate deiminase (EC 3.5.3.13)
   #    score  bias  c-Evalue  i-Evalue hmmfrom  hmm to    alifrom  ali to    envfrom  env to     acc
 ---   ------ ----- --------- --------- ------- -------    ------- -------    ------- -------    ----
   1 !  614.5   4.1  5.7e-189  5.7e-189       4     454 ..       3     446 ..       1     447 [] 0.98

  Alignments for each domain:
  == domain 1  score: 614.5 bits;  conditional E-value: 5.7e-189
                                 TIGR02022   4 yfaerallpdgwaegvrlavaedGrilavetgvsaaedaerlsgvvlpglanlHsHAFqralaGlaeva 72 
                                               ++ e+all +gw+e vrl++a dG+i +ve+gv    ++er   v+lpg++n+HsHAFqra+aGlae +
  lcl|FitnessBrowser__Korea:Ga0059261_3965   3 LWFESALLENGWSERVRLTLA-DGQIETVEAGVDPEAGDERHF-VALPGIPNVHSHAFQRAMAGLAEAR 69 
                                               799*****************9.88*********9877777777.9************************ PP

                                 TIGR02022  73 gsgaDsFWtWRevmYrlverldPeqleaiarqlyvemlkaGftrvgEFHYlHHaadGtpYadpaelaer 141
                                               g+++D+FW+WRe+mYr+v r+ Pe+ eaia+ +y+eml++GftrvgEFHYlHH ++G +Y+d ae++ r
  lcl|FitnessBrowser__Korea:Ga0059261_3965  70 GRADDDFWSWRELMYRFVGRIGPEECEAIAALAYAEMLESGFTRVGEFHYLHHTPGGGRYDDVAEMSGR 138
                                               ********************************************************************* PP

                                 TIGR02022 142 iaaAAadaGigltlLpvlYahagFGgaaanegqrrfiddveaflrlvealrkelaaqeearlGlaiHsl 210
                                               iaaAAa +Gi ltlLpv+Yah gFGg++a ++q rf++dv+ f++lve+   +la  ++ ++G+a+Hsl
  lcl|FitnessBrowser__Korea:Ga0059261_3965 139 IAAAAAATGIALTLLPVFYAHGGFGGQPAGTAQARFLNDVDGFAALVERAGATLA--SDGVIGIAPHSL 205
                                               ***************************************************9998..7889******** PP

                                 TIGR02022 211 RAvtaeelaavlqaserqlPvHiHvaEqqkEvddClaasgrrPvewLldhaevdarwclvHatHltdee 279
                                               RA+t+ el+a+l++++r  PvHiH+aEq kEv dC+a+sg+rPv+wLld+++vdarw+lvHatH+++ e
  lcl|FitnessBrowser__Korea:Ga0059261_3965 206 RAATPGELRALLAMADRG-PVHIHIAEQVKEVADCVAWSGKRPVRWLLDNMPVDARWTLVHATHVEPGE 273
                                               **************9996.************************************************** PP

                                 TIGR02022 280 vkllaksgavaglCpttEanLgDGifpaadfvaaggrlgiGsDshvvvdvleElRllEygqRLrdraRn 348
                                               v+ +a sgavaglCp+tEanLgDG+fpaa+f+a gg +giGsDs+v +d++eElRllEygqRL++raRn
  lcl|FitnessBrowser__Korea:Ga0059261_3965 274 VAGIAASGAVAGLCPITEANLGDGVFPAAEFMALGGMIGIGSDSNVRIDAAEELRLLEYGQRLTRRARN 342
                                               ********************************************************************* PP

                                 TIGR02022 349 vlaageeasvaralydaAlaggaqalGlaegeleaGarADlltldledpslagakgdalldsllfaaek 417
                                               vla g++++++++++ aA+agg ++lG ++ +l+aG +AD+++l+ +dp+ a++kgdal+ds++fa+++
  lcl|FitnessBrowser__Korea:Ga0059261_3965 343 VLAGGDRPATGARMFAAAVAGGGRSLGVET-GLAAGRPADIVSLNRDDPAFAERKGDALVDSWVFASRA 410
                                               ***************************975.7***********************************76 PP

                                 TIGR02022 418 aavrdvvvaGrkvvrdgrHaleeeierafakvlrall 454
                                                 v+ v+++G k+v +grH+++++ie ++  +l++l+
  lcl|FitnessBrowser__Korea:Ga0059261_3965 411 -GVDCVWRGGAKQVAGGRHRDRDAIEARYRVALNRLM 446
                                               .9*******************************9987 PP



Internal pipeline statistics summary:
-------------------------------------
Query model(s):                            1  (455 nodes)
Target sequences:                          1  (447 residues searched)
Passed MSV filter:                         1  (1); expected 0.0 (0.02)
Passed bias filter:                        1  (1); expected 0.0 (0.02)
Passed Vit filter:                         1  (1); expected 0.0 (0.001)
Passed Fwd filter:                         1  (1); expected 0.0 (1e-05)
Initial search space (Z):                  1  [actual number of targets]
Domain search space  (domZ):               1  [number of targets reported over threshold]
# CPU time: 0.02u 0.01s 00:00:00.03 Elapsed: 00:00:00.03
# Mc/sec: 6.38
//
[ok]

This GapMind analysis is from Sep 17 2021. The underlying query database was built on Sep 17 2021.

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About GapMind

Each pathway is defined by a set of rules based on individual steps or genes. Candidates for each step are identified by using ublast (a fast alternative to protein BLAST) against a database of manually-curated proteins (most of which are experimentally characterized) or by using HMMer with enzyme models (usually from TIGRFam). Ublast hits may be split across two different proteins.

A candidate for a step is "high confidence" if either:

where "other" refers to the best ublast hit to a sequence that is not annotated as performing this step (and is not "ignored").

Otherwise, a candidate is "medium confidence" if either:

Other blast hits with at least 50% coverage are "low confidence."

Steps with no high- or medium-confidence candidates may be considered "gaps." For the typical bacterium that can make all 20 amino acids, there are 1-2 gaps in amino acid biosynthesis pathways. For diverse bacteria and archaea that can utilize a carbon source, there is a complete high-confidence catabolic pathway (including a transporter) just 38% of the time, and there is a complete medium-confidence pathway 63% of the time. Gaps may be due to:

GapMind relies on the predicted proteins in the genome and does not search the six-frame translation. In most cases, you can search the six-frame translation by clicking on links to Curated BLAST for each step definition (in the per-step page).

For more information, see the paper from 2019 on GapMind for amino acid biosynthesis, the paper from 2022 on GapMind for carbon sources, or view the source code.

If you notice any errors or omissions in the step descriptions, or any questionable results, please let us know

by Morgan Price, Arkin group, Lawrence Berkeley National Laboratory